Abstract
The truth value of any new piece of information is not only investigated by media platforms, but also debated intensely on internet forums. Forum users are fighting back against misinformation, by informally flagging suspicious posts as false or misleading in their comments. We propose extracting posts informally flagged by Reddit users as a means to narrow down the list of potential instances of disinformation. To identify these flags, we built a dictionary enhanced with part of speech tags and dependency parsing to filter out specific phrases. Our rule-based approach performs similarly to machine learning models, but offers more transparency and interactivity. Posts matched by our technique are presented in a publicly accessible, daily updated, and customizable dashboard. This paper offers a descriptive analysis of which topics, venues, and time periods were linked to perceived misinformation in the first half of 2020, and compares user flagged sources with an external dataset of unreliable news websites. Using this method can help researchers understand how truth and falsehood are perceived in the subreddit communities, and to identify new false narratives before they spread through the larger population.
Highlights
Tracking misinformation has become a task with rapidly increasing importance in recent years
This paper presents a linguistic model integrated in a dashboard that presents data about user-flagged posts from Reddit in real time, measuring Reddit users’ own ability to handle misinformation during a pandemic
We annotated 300 comments (6 × 50) from the full set of 522,320 comments that match the keywords. These comments serve as the test set when comparing the performance of our part of speech (POS) matcher against two machine learning models. These comments were created in a time period that does not overlap with the time period in which we developed the POS matcher; we can argue that it serves as a true test set, with data that could not have influenced the way we built the POS patterns
Summary
Tracking misinformation has become a task with rapidly increasing importance in recent years. Massive disinformation campaigns influencing the U.S 2016 elections and the Brexit referendum have suggested that the spread of false information can have a large scale political impact [1,2]. Even outside a U.S or UK context, in the last five years, democratic regimes have become more vulnerable to foreign influence efforts directed to sow mistrust in authorities [3,4,5]. During the COVID-19 pandemic, false information can influence the well-being and life of many. In the midst of rising infection rates, narratives misrepresenting official responses to the virus are among the most common in recent news [6]. The problem has become so widespread that the World Health
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